Quince is looking to revolutionize the way people purchase essential goods through cutting-edge data science and AI solutions, specifically by optimizing and automating decision-making processes and delivering actionable business insights to drive growth and enhance marketing efficiency.
Requirements
- MS or PhD in statistics, mathematics, engineering, computer science, economics or another quantitative field
- Hands-on experience applying machine learning, predictive modeling, causal inference to drive growth.
- Deep knowledge in statistical and machine learning techniques, and causal inference.
- Deep knowledge in Data Science libraries in a programming or scripting language.
- Deep knowledge in growth and digital marketing.
- Proficient in Python and SQL.
- Experience with BI platforms such as Looker, Tableau etc.
Responsibilities
- Develop and implement predictive models and advanced analytics to understand customer behavior, lifetime value, and churn.
- Utilize statistical and machine learning techniques to optimize marketing campaigns and personalization efforts.
- Develop and refine customer segmentation models to enable targeted marketing efforts.
- Develop and refine attribution models to understand the impact of different marketing channels and touch points on customer conversion and validate the model accuracy.
- Optimize budget allocation on paid channels to maximize ROI.
- Design, execute, and analyze A/B tests and other experiments to validate hypotheses and inform growth strategies.
- Work closely with engineering, product, and marketing teams to integrate data-driven insights into decision-making processes.
Other
- Move fast, be a team player, and kind.
- Excellent communication and presentation skills.
- Strong problem-solving skills with the ability to think critically and strategically about business challenges.
- 5+ years of experience as a growth data scientist in relevant industry.
- Candidates based in the SF Bay Area will be required to work in a hybrid capacity out of our Palo Alto office, 3 days/week (Monday, Wednesday and Thursday).
